Gene Matching Using JBits

1
Gene Matching Using JBits

• Steven A. Guccione
• Eric Keller

2
String Matching

• At least nine independent discoveries of the
  dynamic programming algorithm for minimum edit
  distance published in the early 1970s
• Useful for many types of problems (speech
  recognition, typography, geology, etc )
• Renewed interest with the beginning of the Human
  Genome Project in 1990

3
Gene Matching

• Four character alphabet from four bases in DNA
  sequences adenine (A), thymine (T), cytosine
  (C), and guanine (G)
• Matching in presence of character insertions and
  deletions required
• Matching of protein sequences also of interest
• Several matching algorithms currently in use
• 3 billion bases in the human genome

4
Smith-Waterman Algorithm

• Optimal edit distance calculation
• Position independent
• O(nm) complexity

5
A Smith-Watermann Example

• Compare strings Tmail and Smale
• Set substitution cost 2, insert / delete costs
  1
• Perform calculations starting at (T0, S0)
• Final edit distance at (Tn, Sm) 2
• O(nm) operations

6
A Smith-Watermann Example
7
Exploiting Parallelism

• Recurrence dependencies limit parallelism
• Parallelizing along diagonals possible
• Can use N processing units
• Requires time proportional to M

8
Parallelism Along Diagonals
9
A JBits Implementation

• JBits permits rapid configurable circuit
  implementation
• Easily parameterized circuit elements
• Good for highly repetitive structures
• Portable across devices of different sizes
• Permits dense circuit implementation

10
Logic Implementation
Si

Tj

2
a
min
d
b

1
min
c

1
4LUT pair
11
Implementation Details

• Sj string values can be folded into circuit
• Addition constants also folded in
• Total logic circuit uses six four-input Look-Up
  Tables (4LUTs)
• Further optimizations possible

12
The Parameterizable Circuit
Tin
Tout
Tj
Din
a
Dout
d
c
b
INITin
INITout
13
Datapath Width

• Output values change by 0, 1 or 2 (Lipton and
  Lopresti)
• Two bits are enough to represent calculations
• Datapath width independent of string length
• Final edit distance easily derived from string of
  two-bit values using a counter
• Initialize counter to string length
• if (dt1 dt 1) count up, else count down

14
Further Optimizations

• d always equals a or (a2)
• d0 is always the same as a0
• b and c always equals a1 or a-1
• only most significant bit of each is necessary
• Function becomes a wide or
• Design can be mapped to carry chain logic
• Final optimized circuit uses six flip-flops, five
  4LUTs and carry chain logic
• Uses three LUT-FF pair slices

15
Further Circuit Optimizations
dout
t0out
t1out
t0in
ltgt
0 1
t1in
s0
s1
1
din
a1 bc
0 1
1
0
0 1
0
INITout
INITin
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The Array
GCAGTTGCA...
Data in
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RTR Advantages

• No flip-flops needed to store string
• No time spent loading string
• Simpler IO / interfacing
• Smaller circuits
• Faster circuits
• Lower power

18
RTR vs. Static Design

• Splash II (VHDL) 33.33 LUT/FF pairs per
  processing unit
• JBits 6 LUT/FF pairs per processing unit
• No time required to pre-load match string
• Data and circuit loaded via configuration bus
• Result read back via configuration bus
• No IOBs or special interfacing required

19
Comparisons
20
Conclusions

• Modern FPGAs provide fast, efficient gene
  matching implementations
• A single FPGA can replace hundreds of high-end
  compute servers
• Run-time reconfiguration (RTR) provides speed,
  density, power and interfacing advantages